hcppcv: Perform the HCP normalization algorithm on a grid of model...

In mvaniterson/Rhcpp: Normalizing RNA-Sequencing Data by Modeling Hidden Covariates with Prior Knowledge plus extentions to perform EWAS and TWAS

Description

Perform the HCP normalization algorithm on a grid of model parameters

Usage

hcppcv(Z, Y, X, kRange = c(10, 20), lambdaRange = NULL,
  lambda1Range = NULL, lambda2Range = NULL, lambda3Range = NULL,
  performance = NULL, iter = 100, stand = TRUE, log = TRUE,
  verbose = TRUE, fast = TRUE)

Arguments

Z	a matrix nxd of known covariates, where n is the number of subjects and d is the number of known covariates. *must be standardize (columns have 0 mean and constant SS).
Y	a matrix of nxg of expression data (must be standardized (columns scaled to have constant SS and mean 0). ** use standardize function to standardize F and Y.
X	vector of responses.
kRange	multiple numbers of inferred hidden components (k is an integer)
lambdaRange	multiple model parameters
lambda1Range	multiple model parameters
lambda2Range	multiple model parameters
lambda3Range	multiple model parameters
performance	function accepting res with res$Res the transformed Residuals
iter	(optional) iter: number of iterations (default = 100);
stand	default standardize data TRUE
log	default log-transformation TRUE
verbose	default TRUE
fast	default use fast RcppArmadillo implementation

Details

This function can be used to find the optimal model parameters with a used-defined performance function

Value

vector of performance measures with names indicating the model parameter

Author(s)

mvaniterson

Examples

## Not run: 
library(BiocParallel)
library(Rhcpp)
register(MulticoreParam(3))
kRange <- c(10, 20)
lambdaRange <- c(1, 5, 10, 20)
data(rhcppdata)
F <- rhcppdata$F
Y <- rhcppdata$Y
## we do not have response for this data
x <- rnorm(nrol(Y))
##not really meaning full performance function
res <- hcppcv(F, Y, X, kRange, lambdaRange, performance=function(res) sum(res$Res))
res

## End(Not run)

mvaniterson/Rhcpp documentation built on Feb. 24, 2020, 4:06 p.m.